AI Full Stack Architect

AI Full Stack Architect

12 months contract

Atlanta, GA (Hybrid)


About the Role

We are seeking an AI Full Stack Architect to join our AI Center of Excellence. This is a senior individual-contributor and technical leadership role for someone who has spent years in the trenches building, shipping, and scaling AI-powered systems and is now ready to set the architectural direction for the next generation of intelligent applications.

You will own the end-to-end design and delivery of agentic AI systems, LLM-powered platforms, and full stack applications that operate at enterprise scale. You bring deep hands-on expertise across AWS Bedrock, Python, Node.js, and modern front-end frameworks, and you know how to translate that into production-grade architecture that others can build on.

Level & Scope



DIMENSION

EXPECTATION

Seniority

Architect / Principal senior IC with org-wide technical influence

Experience Bar

10+ years in software engineering; 4+ years in AI/ML engineering

Depth

Expert-level in at least two of: agent development, LLM integration, cloud-native backend

Breadth

Fluent across the full stack infra, backend, AI layer, and front-end

Leadership

Drives architecture decisions, mentors engineers, sets standards

Ambiguity

Comfortable defining the problem, not just solving it

Impact

Platform-level your decisions affect multiple teams and products

Key Responsibilities

Agent Architecture & Development

Architect and build autonomous, multi-step, and tool-using AI agents using AWS Bedrock Agents and leading agentic frameworks.

Design multi-agent orchestration topologies including supervisor/worker patterns, parallel execution, and handoff protocols.

Establish agent design patterns: memory management, context windows, tool-call sequencing, retry logic, and failure recovery.

Define guardrails, safety layers, and responsible AI standards for all agent-based systems.

LLM / SLM Integration

Own the selection, integration, and lifecycle management of Large Language Models (Claude, GPT-4, Llama, Mistral) and Small Language Models (Phi-3, Gemma, Mistral 7B).

Design and implement RAG pipelines, prompt engineering standards, few-shot frameworks, and fine-tuning workflows.

Establish model benchmarking and evaluation criteria balancing capability, latency, cost, and safety.

Cloud & Infrastructure (AWS)

Lead the design of AI infrastructure on AWS Bedrock including foundation model access, Knowledge Bases, and Bedrock Agents.

Architect cloud-native backend services using Lambda, API Gateway, ECS/EKS, S3, and IAM.

Define infrastructure-as-code standards and CI/CD pipelines for AI workloads.

Full Stack Development

Build and architect full stack applications React/Next.js front ends, Node.js backend services, and Python agent/ML pipelines.

Design streaming agent UIs, chat interfaces, and real-time AI-powered user experiences.

Own API design and integration patterns between front-end, backend, and AI layers.

Monitoring, Observability & Evaluation

Implement agent monitoring and evaluation pipelines using Fiddler AI and comparable platforms (LangSmith, Arize, Weights & Biases, Helicone).

Define and track agent performance metrics: accuracy, latency, hallucination rate, tool-call success rate, and cost-per-interaction.

Build feedback loops that drive continuous model and agent improvement.

Technical Leadership

Lead architecture reviews, design documents, and RFC processes for AI initiatives.

Mentor and upskill engineers on agent development, LLM integration, and AI best practices.

Partner with product, data, and platform teams to translate business requirements into scalable AI solutions.

Continuously evaluate emerging models, frameworks, and tooling bringing the best to the team.

Required Qualifications

AWS & Cloud

AWS Bedrock deep, hands-on experience building and deploying agents and models (Bedrock Agents, Knowledge Bases, foundation model APIs).

Strong working knowledge of the broader AWS ecosystem: Lambda, S3, IAM, API Gateway, ECS/EKS, CloudWatch.

Experience designing cloud-native, serverless, and containerized AI workloads